Achieving autonomy in robotics has been a continuing objective, and an ongoing topic in research and development for decades. The implications of successful implementations are often far reaching. This is the case in mobility, where both people, goods and vehicles need to be moved safely, efficiently and quickly. For mobile autonomous robots to become a reality, they need to perceive the world around them, in order to operate.
Recent advancements in machine vision technology has brought autonomy closer to realization. Cameras, LiDAR and RADAR (among others) provide the robots with very rich data from their surroundings. This however comes with challenges. The volume of data collected by these real-time systems quickly becomes a burden on the limited memory and computational resources on board. Processing all of the acquired data as it is being generated, to enable the robot to act in its environment, becomes expensive and burdensome.
Pre-made maps of the infrastructure in the environment where the robot or vehicle is traveling can be useful to alleviate the real-time requirements of these systems. In addition, maps can help the robot anticipate the oncoming infrastructure to better plan its routes.
One application of mobile autonomous robots is roadway travel, where vehicles carry people and goods on road networks. Similarly, autonomous trains travel along railway infrastructure. Aerial vehicles, large or small, can also take advantage of autonomy. In these applications and many more, it may be desirable for such vehicles to operate on massive continental scales, meaning that they would travel continuously along the infrastructure over long distances, without requiring human intervention.
While creating a 3D semantic map of a test track for research purposes in autonomy is a rather well-understood practice in surveying, creating these maps for large areas remains a challenge. Use of traditional methods of surveying and digitizing thousands of kilometers of road or rail infrastructure is costly, time-consuming and operationally complex. This is due to factors such as the high levels of accuracy required in the resulting maps, the number of assets to be noted in the environment, and the size of the data used to generate these maps. In addition, with constant changes in the environment, certain areas need to be remapped, which adds to the complexity of the problem.